You can deploy this model and start earning money today!

JosianaSilva/
santander-customer-prediction

Name: santander-customer-prediction
Availability: InStock

Views

No views yet

Santander Customer Transaction Prediction

Modelo para predição de transações de clientes baseado no dataset do Kaggle Santander Customer Transaction Prediction.

Este modelo foi treinado usando Gradient Boosting Classifier com redução de dimensionalidade via PCA.

Metrics

Accuracy: 0.9138 (91.38%)
Roc Auc: 0.6275 (62.75%)
Precision: 0.6777
Recall: 0.2693
F1 Score: 0.3855

Deployment Criteria

This model was automatically deployed because it meets the following criteria:

Accuracy > 90%
ROC AUC Score > 60%

Only models that meet these performance thresholds are deployed to ensure quality.

Usage

python

from huggingface_hub import hf_hub_download
import joblib

# Download
model_path = hf_hub_download(repo_id="your-repo", filename="model.pkl")
pca_path = hf_hub_download(repo_id="your-repo", filename="pca.pkl")
scaler_path = hf_hub_download(repo_id="your-repo", filename="scaler.pkl")

# Load
model = joblib.load(model_path)
pca = joblib.load(pca_path)
scaler = joblib.load(scaler_path)

# Predict (assume X is your input data)
X_scaled = scaler.transform(X)
X_transformed = pca.transform(X_scaled)
predictions = model.predict(X_transformed)
probabilities = model.predict_proba(X_transformed)

Model Architecture

Algorithm: Gradient Boosting Classifier
Dimensionality Reduction: PCA (95% variance retained)
Preprocessing: Standard Scaler
Features: 200 anonymous features from Santander dataset

Training Details

Train/Test Split: 80/20
Random State: 42 (for reproducibility)
Cross-validation: Used during hyperparameter tuning

JosianaSilva/santander-customer-prediction